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Update README.md

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README.md CHANGED
@@ -29,8 +29,13 @@ A Hugging Face Gradio application that generates CLIP-ready visual descriptions
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  1. Go to [Hugging Face Spaces](https://huggingface.co/spaces)
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  2. Click "Create new Space"
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  3. Choose "Gradio" as the SDK
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- 4. Upload `app.py` and `requirements.txt`
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- 5. Your app will be deployed automatically!
 
 
 
 
 
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  ### 2. Prepare Your CSV Files
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@@ -46,15 +51,16 @@ Equipment,additional_data
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  ### 3. Use the Application
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- 1. **Login**: Click the login button and authenticate with your Hugging Face account
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- 2. **Upload Files**: Upload one or more CSV files
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- 3. **Specify Column**: Enter the name of the column containing categories (default: "category")
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- 4. **Adjust Settings** (optional):
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  - Max Tokens: 64-512 (default: 256)
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  - Temperature: 0.1-1.0 (default: 0.7)
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  - Top-p: 0.1-1.0 (default: 0.9)
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- 5. **Process**: Click "Process Files" and wait for completion
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- 6. **Download**: Download the output CSV files with descriptions
 
 
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  ## Output Format
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@@ -88,13 +94,19 @@ Car Rental For Self Driven,"a car available for self-drive rental, parked at a p
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  ## Troubleshooting
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  ### "Column not found" error
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  - Check that the column name matches exactly (case-sensitive)
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  - View the error message to see available columns
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- ### "Please login" error
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- - Make sure you're logged in with a valid Hugging Face account
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- - Check that your account has access to the Inference API
 
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  ### Slow processing
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  - The model processes each unique category individually
@@ -106,10 +118,22 @@ Car Rental For Self Driven,"a car available for self-drive rental, parked at a p
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  To run locally:
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  ```bash
 
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  pip install -r requirements.txt
 
 
 
 
 
 
 
 
 
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  python app.py
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  ```
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  ## License
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  This project uses the Llama 3.3 model which requires agreement to Meta's license terms.
 
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  1. Go to [Hugging Face Spaces](https://huggingface.co/spaces)
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  2. Click "Create new Space"
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  3. Choose "Gradio" as the SDK
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+ 4. Upload `app.py`, `requirements.txt`, and `README.md`
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+ 5. **Enable OAuth (Required)**:
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+ - Go to your Space's Settings
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+ - Scroll to "OAuth" section
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+ - Enable "OAuth" toggle
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+ - This allows the app to use your HF Pro account's API access
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+ 6. Your app will be deployed automatically!
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  ### 2. Prepare Your CSV Files
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  ### 3. Use the Application
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+ 1. **Upload Files**: Upload one or more CSV files
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+ 2. **Specify Column**: Enter the name of the column containing categories (default: "category")
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+ 3. **Adjust Settings** (optional):
 
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  - Max Tokens: 64-512 (default: 256)
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  - Temperature: 0.1-1.0 (default: 0.7)
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  - Top-p: 0.1-1.0 (default: 0.9)
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+ 4. **Process**: Click "Process Files" and wait for completion
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+ 5. **Download**: Download the output CSV files with descriptions
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+
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+ *Note: Authentication is handled automatically through HF Spaces OAuth when enabled in settings.*
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  ## Output Format
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  ## Troubleshooting
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+ ### "Hugging Face token not found" error
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+ - Make sure OAuth is enabled in your Space settings
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+ - Go to Space Settings → OAuth → Enable OAuth toggle
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+ - Restart your Space after enabling OAuth
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+
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  ### "Column not found" error
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  - Check that the column name matches exactly (case-sensitive)
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  - View the error message to see available columns
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+ ### Authentication errors
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+ - Ensure you have a Hugging Face Pro account for API access
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+ - Check that OAuth is properly configured in Space settings
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+ - Verify that your account has access to the Llama 3.3 model
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  ### Slow processing
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  - The model processes each unique category individually
 
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  To run locally:
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  ```bash
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+ # Install dependencies
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  pip install -r requirements.txt
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+
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+ # Set your Hugging Face token as an environment variable
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+ # Windows (PowerShell):
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+ $env:HF_TOKEN="your_hf_token_here"
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+
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+ # Linux/Mac:
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+ export HF_TOKEN="your_hf_token_here"
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+
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+ # Run the app
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  python app.py
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  ```
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+ Get your token from: https://huggingface.co/settings/tokens
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+
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  ## License
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  This project uses the Llama 3.3 model which requires agreement to Meta's license terms.